January 2018 at a Glance: Biomarkers, Co-Morbidities and Mechanical Circulatory Support
Bibliographic record
Abstract
A position paper from the Heart Failure Association (HFA) reviews the prognostic significance of cardiopulmonary exercise testing in specific groups of patients with heart failure (HF), including the elderly, patients with obesity or other co-morbidities, preserved ejection fraction (HFpEF), permanent atrial fibrillation, those on mechanical circulatory support (MCS). 1 Another HFA position paper reviews the prevalence, diagnosis, pathophysiology and possible treatments of right heart dysfunction in HFpEF. 2 Lastly, Nagueh reviews parameters and methods for the non-invasive assessment of left ventricular end-diastolic pressure.3 Recent data have been published and the accuracy of non-invasive measurements is still a matter of analysis.4,5 Biomarkers: the case of microRNAsThree articles add data about the role of microRNAs as prognostic markers in HF. 6,7 Bayés-Genis et al. 8 measured 12 microRNAs in two independent cohorts of HF patients.Circulating miR-1254 and miR-1306-5p were associated with outcomes but did not improve prognostic assessment over established prognostic variables.Masson et al. 9 show that circulating miR-132 levels improve risk prediction for HF readmission beyond traditional factors but lack of additional predictive value for mortality.Lastly, serially measured, but not baseline, miR-1306-5p was predictive of poorer outcomes in patients with acute HF and had an additive value beyond natriuretic peptides.10 Co-morbidities and medical treatment Chronic obstructive pulmonary diseaseLung function is tightly related with HF. 11 Chronic obstructive pulmonary disease coexisted with HF in more than 15% of patients in the European Society of Cardiology HF Long-Term Registry.At multivariable analysis, it had a significant impact on treatment and HF hospitalization rates but not on mortality.12 These results confirm previous data.13 -15 . . . . . . . . . . .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".